Here, GIS capabilities were used for monitoring the results of the neuro-fuzzy network as the raster layer of groundwater quality and finally the results are presented in Fig. 9.
Figure 3 shows raster plots of network firings for the first 1 second of simulations with three different levels of the external input.
a Raster plot of network firing for simulation with N = 800, h = w N 27, α = 8 w N 27, w = 0.01.
Figure 13 shows raster plots of network firings when the system is driven from a subcritical and supercritical state with h = 1 / N. Compared with the critical case, see Fig. 3(a), with the subcritical parameter set the bursts appear to be shorter and consist of fewer neurons firing.
Figure 4B depicts the spike timing raster of the network, and Figure 4C shows the resultant connectivity matrix, obtained at the end of the simulation (t = 10 s), averaged over 100 trials.
a) One-minute raster plots of network activity recordings in normal medium (left) and carbachol (CCh; right) showing distinctly different activity patterns.
Fig. 5 shows true road distances from the 150 farmhouses to their entrance on a road in our network, as estimated from the raster images, against the Euclidean distance of the farmhouses to their nearest node in the network (in general, the nearest node does not correspond to the position of farm entrance onto a road in our network).
A variety of topographic data sources that include contour maps, cross-sections or topographic profiles, raster, triangulated irregular networks (TINs), and point clouds can be integrated as legacy data into these types of analyses.
Fig. 3 Raster plots of the network dynamics for different levels of the external input.
Deleted neurons no longer contributed to running-time histograms of network activity and were removed from raster plots (e.g., Figure 6C).
